A new Level-set based Protocol for Accurate Bone Segmentation from CT Imaging

TL;DR

This paper introduces a two-step, level-set based protocol for precise bone segmentation from CT images, combining user input and automatic refinement to achieve high accuracy in medical imaging.

Contribution

It presents a novel segmentation pipeline that integrates user pre-segmentation with automatic refinement steps, improving accuracy and robustness in bone segmentation from CT scans.

Findings

Achieved high sub-pixel accuracy in femur segmentation.

Effective across different surface meshing strategies.

Validated against a high-precision physical model.

Abstract

In this work it is proposed a medical image segmentation pipeline for accurate bone segmentation from CT imaging. It is a two-step methodology, with a pre-segmentation step and a segmentation refinement step. First, the user performs a rough segmenting of the desired region of interest. Next, a fully automatic refinement step is applied to the pre-segmented data. The automatic segmentation refinement is composed by several sub-stpng, namely image deconvolution, image cropping and interpolation. The user-defined pre-segmentation is then refined over the deconvolved, cropped, and up-sampled version of the image. The algorithm is applied in the segmentation of CT images of a composite femur bone, reconstructed with different reconstruction protocols. Segmentation outcomes are validated against a gold standard model obtained with coordinate measuring machine Nikon Metris LK V20 with a digital line scanner LC60-D that guarantees an accuracy of 28 $\mu m$. High sub-pixel accuracy models were obtained for all tested Datasets. The algorithm is able to produce high quality segmentation of the composite femur regardless of the surface meshing strategy used.